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Cited by 93D Shape Modeling and AnalysisGenerative Adversarial Networks and Image SynthesisHuman Motion and Animation

AnyTop: Character Animation Diffusion with Any Topology

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Gat, Inbar, Raab, Sigal, Tevet, Guy, Reshef, Yuval, Bermano, Amit Haim, Cohen-Or, Daniel·2025·Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers
DOI 10.1145/3721238.3730621
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Abstract

Generating motion for arbitrary skeletons is a longstanding challenge in computer graphics, remaining largely unexplored due to the scarcity of diverse datasets and the irregular nature of the data. In this work, we introduce AnyTop, a diffusion model that generates motions for diverse characters with distinct motion dynamics, using only their skeletal structure as input. Our work features a transformer-based denoising network, tailored for arbitrary skeleton learning, integrating topology information into the traditional attention mechanism. Additionally, by incorporating textual joint descriptions into the latent feature representation, AnyTop learns semantic correspondences between joints across diverse skeletons. Our evaluation demonstrates that AnyTop generalizes well, even with as few as three training examples per topology, and can produce motions for unseen skeletons as well. Furthermore, our model's latent space is highly informative, enabling downstream tasks such as joint correspondence, temporal segmentation and motion editing. Our webpage, https://anytop2025.github.io/Anytop-page, includes links to videos and code.

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  • The BlendAnything design combines the factorized representation of Diffusion Autoencoders (DiffAE) with the any-topology motion generation setting of AnyTop.

    “The design combines the factorized representation of Diffusion Autoencoders (DiffAE) [Preechakul et al. 2022] with the any-topology motion generation setting of AnyTop [Gat et al. 2025].”

    From BlendAnything: A Blender Plugin for Cross-Topology Motion Blending (2026) · cites this paper as Gat et al. 2025

  • Cross-topology retargeting and topology-agnostic motion generation have expanded the range of characters that can be animated.

    “cross-topology retargeting and topology-agnostic motion generation have expanded the range of characters that can be animated [Chen et al. 2025; Gat et al. 2025]”

    From BlendAnything: A Blender Plugin for Cross-Topology Motion Blending (2026) · cites this paper as Gat et al. 2025

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Cited by

Sorted by citations

Authors
Gat, I., Raab, S., Tevet, G., Reshef, Y., Bermano, A. H., & Cohen-Or, D.
Affiliations
  • Tel Aviv University (IL)
  • Tel Aviv University, Tel Aviv, Israel
ORCID
Venue
Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers
Publisher
ACM
Published
2025-07-27
Pages
1-10
DOI
10.1145/3721238.3730621
OpenAlex
W4412595426
Cited by
9
References
3
Citations by year
2026: 7 · 2025: 2
FWCI
8.61
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • ACM SIGGRAPH Conference (SIGGRAPH) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
Tel Aviv University
Topics
  • Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
  • Physical Sciences › Engineering › Control and Systems Engineering › Human Motion and Animation
Keywords
character animation, diffusion models, 3D character animation, generative models

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